The purpose of this white paper is to provide the life science industry with an approach to process validation that provides a method to identify what variables exist in the process, which variables exhibit dominance, and control methods to minimize process variability using the concepts of Dominance as described by Dr. Juran.
Process validation as defined by the FDA (21 CFR 820.3(z) (1)), “Process validation means establishing by objective evidence that a process consistently produces a result or product meeting its predetermined specifications.”
Past industry consensus on what would be considered a successful process validation was 3 successful concurrent lots, specifications met and the process was validated. The expectation was that we ran within the range of the established parameters as identified in characterization or previous qualification testing and if we met product specification we passed the validation, accepted the process and proceeded to commence manufacturing for sale batches.
This is no longer the accepted view of process validation based on the number of guidance documents from ICH, GHTF, and FDA which focuses on information and knowledge from product and process development. This knowledge and understanding is the basis for establishing an approach to control that is appropriate for the manufacturing process. Manufacturers should:
- understand the sources of variation
- detect the presence and degree of variation
- understand the impact of variation on the process and ultimately on product attributes
- control the variation in a manner commensurate with the risk it represents to the process and product
How do we get from 3 batches and done to this broader interpretation of variation, control strategies and risk methods?
Juran identifies that operating processes are influenced by many variables, but often one variable is more important than all of the rest combined. Such a variable is said to be the “dominant variable.” Knowledge of which Process variable is dominant helps planners during allocation of resources and priorities. The more usual dominant variables include:
Set-up dominant: Some processes exhibit high stability and reproducibility of results, over many cycles of operation. A common example is the printing process. The design for control should provide the operating forces with the means for precise setup and validation before operations proceed. Examples of such processes are drilling, labeling, heat sealing, printing, and presswork.
Time-dominant: Here the process is known to change progressively with time, for example, depletion of consumable supplies, heating up, and wear of tools. The design for control should provide means for periodic evaluation of the effect of progressive change to enable the worker to make compensatory changes. Screw machining, volume filling, wood carding, and papermaking are examples of time-dominant processes.
Component-dominant: Here the main variable is the quality of the input materials and components. An example is the assembly of electronic or mechanical equipments. The design for control should be directed at supplier relations, along with incoming inspection and sorting of inferior lots. Many assembly operations and formulation processes are component-dominant.
Worker-dominant: In these processes, quality depends mainly on the skill and knowledge possessed by the workers. The skilled trades are well-known examples. The design for control should emphasize aptitude testing of workers, training and certification, quality rating of workers, and error-proofing to reduce worker errors. Workers are dominant in most manual processes such as welding, painting, and order picking.
Information-dominant: Here the processes are of a “job-shop” nature, so that there is frequent change in what product is to be produced. As a result, the job information changes frequently. The design for control should concentrate on accuracy and up-to-dateness of the information provided to the worker (and everyone else). Examples include order editing and “travelers” used in job shops, ERP Systems and other computer aided information systems and networks.
Typical Control Solutions
Solutions as identified by Juran include :
Inspection of process conditions, First piece inspection, Lot plot, Precontrol, Narrow limit gauging,Attribute visual inspection
Periodic inspection, X_ chart,
Median chart, X_ and R chart,
Precontrol, Narrow-limit gauging,
p chart, Process variables check,
Automatic recording, Process audits
Supplier rating, Incoming inspection
Prior operation control, Acceptance inspection, Mockup evaluation
Acceptance inspection, p chart, c chart, Operating scoring, Recertification of workers, Process audits
Computer-generated information “Active” checking of documentation, Barcodes and electronic entry, Process audits
Proposed Solution Using Process Mapping and Evaluation of the Process
Develop a process map of the process undergoing validation. Referencing this map identify the process inputs and outputs associated with each manufacturing step. Once complete, evaluate which of the 5 typical variations are involved with each step. Then assess the variables to determine which variable is the dominant variable for each step of the process (as applicable). [1st Pass]
Control/ Mitigation strategy
For each of the variables identified in the process map assessment using the example control strategies identify controls that reduce the potential variability. Once the control/mitigation strategies have been invoked perform a second assessment to ensure the variables and dominate variable for each step remains appropriate. [2nd Pass]
Develop test cases that examine each of the dominant sources of variability in the process as defined in the process map. Remember to evaluate each control strategy or mitigation which was implemented to reduce variability and increase control.
This solution provides a repeatable process for the life science end user to identify, document, evaluate and control variation in the process subject to the process validation study. The end user using this process should be able to:
- Through process mapping identify and understand the sources of variation.
- Evaluate the different types of variation that may be present in the process and identify which variation is dominant.
- Evaluate the impact of variation on product and process performance. Through evaluation of the dominance factors on the Critical Process Parameters and their relation to the Critical Quality Attributes of the product.
- Control the variation in a manner commensurate with the risk it represents to the process and product.
About the Author
Rick Van Doel is a Principal Validation Specialist with Performance Validation and the President/CEO of Performance Validation. He has been providing services to the Life Science Industry since 1993, and has experience in the delivery of commissioning, qualification and validation associated with pharmaceutical and medical device projects.
 Juran’s Quality Handbook (Edition 5)
 Process Validation: General Principles and Practices